What we believe

What we believe

"Here's to the crazy ones. The misfits, the rebels, the troublemakers, the round pegs in the square holes, the ones who see things differently. They're not fond of rules, and they have no respect for the status quo. You can quote them, disagree with them, glorify or vilify them. About the only thing you can't do is ignore them. Because they change things - they push the human race forward. And while some may see them as the crazy ones, we see genius. Because the people who are crazy enough to think they can change the world, are the ones who do." --Steve Jobs

CogAI4Sci team

CogAI4Sci team

Our Cognitive AI for Science (CogAI4Sci) team at National University of Singapore focuses on developing novel AI methods using inspiration from cognitive sciences and using the methods to solve real-world problems in biomedical sciences and healthcare. Recently we are focusing on 1)discrete compositional representation,on the foundamental machihe learning side, and 2) predicts treatment and intervention effects in biomedical systems,on the machine learning for science side.

Before starting CogAI4Sci team, Dianbo Liu was a group leader at the Broad Institute of MIT and Harvard. Prior to the Broad Institute, Dianbo worked as a postdoctoral researcher with Prof. Yoshua Bengio (a Turing Award winner) and led the Humanitarian AI team at the Mila-Quebec AI Institute. This followed his fellowship training and studies in medical informatics at Harvard Medical School. Dianbo earned his PhD from the University of Dundee, Scotland, under the supervision of Prof. Timothea Newman. During his doctoral studies,he received the Vest Scholarship from the Massachusetts Institute of Technology (MIT) and was a special graduate student at the MIT Computer Science and Artificial Intelligence Lab. Dianbo also co-founded two start-ups, "GeneTank" and "SecureAILabs," to advance AI applications in biomedical sciences during his training.

Research aims

Research aims

Our team focus on two branches of research: 1) Cognitive science inspired machine intelligence. and 2) AI for biomedical sciences

Together with other researchers, we aim at transforming Singapore into a leading AI technology export hub in Asia.

The ultimate question we like to answer is what separates human intelligence from other species in the evolutionary process and how to use the knowledge to build self-concious AI that can learn, train, understand and create independent of human intervention.

Among the differences between Homo Sapiens and other species, an very important one is the usage of discrete symbols. Therefore, in recent years, our team is focusing on understanding,optimization and usage of discrete compositional representations to solve generalization and reasoning problems in machine learning.

Develop the next generation of machine intelligence thinking like a scientist to analyze and comprehend the growing volume and diversity of biomedical data, with the aim of doubling the average life expectancy worldwide for all.

Currently, our team is focusing on developing novel AI methods to estimate treatment effects and intervention responses in medicine, such as how a patient responses to a surgery,and biological settings such as cells in each part of our body responds to a drug. These will help us improve efficiency of healthcare and discovery scientific knowledge.



Abhijeet Sinha (PhD candidate, previously IIT, Madras, India)

Aryan Amit Barsainyan (intern from NITK, India )

Anirudh Prabhakaran (Intern from NIT,India. )

Chengbo Li(Intern, From UIUC, USA.)

Dianbo Liu (Principal investigator)

Hongyu He ( PhD candidate. Previously Duke Uni, USA.)

Jiawei Wu (Intern, from Huadong Normal Uni.,China. )

Mingyuan Yan (Graduate research asistant. Previous: Huadong Normal Uni.,China. )

Mike Zhu(co-advised PhD student with Prof.Yue Li at McGill)

Nirlipta Pande (intern, from BITS, Pilani, India)

Peisong Zhang (master student at NUS)

Qiran Zou (Graduate research asssitant, previously Tsinghua Uni., China)

Rushi Shah (Intern, from IIT, Jodhpur, India.)

Srinivas Anumasa (Postdoc researcher. Previously IIT Hyderabad, India. )

Sankepally Sainath Reddy (Intern, from IIIT-RAIPUR ,India. )

Trang Nguyen Ngoc Phuong (Graduate research asssitant, previously Mila Canada)

Ting Xu (Postdoc researcher, co-mentor with Prof. Ching-Yu Cheng. Previously University of Science and Technology of China)

Tingting Chen (PhD candidate. Previous: University of New South Wales,Australia.)

Wenhao Zhao (PhD candidate. Previously Beihang Uni.,China. )

Xuming Ran (Graduate research assistant. Previously, Chongqing Jiaotong and Shanghai AI)

Yiming Tang(PhD candidate. Previous: Peking University, China.)

Yuxuan Wu (Visiting scholar from ShanghaiHaishi University, China.)

Yizhen Qin (Intern from Tsinghua University, China.)

Zarif Ikram (Visiting scholar from Bangladesh University of Engineering and Technology. )

Zarif Bin Akhtar (Intern from American International University-Bangladesh. )

Zhixuan Xiao (visiting scholar from Tsinghua Uni., China. )

Xiaoye Wang (Intern, Harbin Institute of Tech.,China. Next: Cambridge Uni. )

Uma Kadam (Intern, from IIIT Guwahati, India. Next: Microsoft )

Abhinav_Sharma (Intern, from IIIT Guwahati, India. Next: back to Inida )

Manvith Prabhu (Intern, from NITK, India. Next: back to Inida )

Taoyong Cui (Intern from Tsinghua University, China. Next: back to Tsinghua)

Anhying Bai (Intern, from Tsinghua University, China. Next: back to Tsinghua)

Zheqi Liu (From Tsinghua Uni.,China. Intern. Next: UCSD)

Maab Elrashid (From Sudan. Mentee at Mila-Quebec AI institute, with Prof. Yoshua Bengio. Next: Mila)

Ziqing Mai(From Tsinghua University, China. Intern. Next: back to Tsinghua)

Zile Yang(Intern, from Huazhong tech University, China. Next: back to Huangzhong )

Yice Fang(From Tsinghua University, China. Intern. Next: back to Tsinghua)

Bonaventure F. P. Dossou (Mentee at Mila-Quebec AI institute. next: PhD at McGill )

Rulin Shao (Mentee at MIT/Harvard, Next graduent student at CMU/ Amazon, now PhD at UW)

Loïc Kwate Dassi (Mentee at Mila-Quebec AI institute, Next: DeepMind London )

Oussama Boussif, Mentee at Mila. Next: PhD with Yoshua Bengio at Mila )

Li Huang (Mentee at Harvard. Next: PhD at Tsinghua. Now: faculty at Chinese Academy of Medical Sciences & Peking Union Medical College )

James Assiene (Mentee at Mila-Quebec AI institute. Next: DeepMind London )

Tianyi Zhang (Mentee at Harvard. Next: PhD at ASU )

Léna Néhale Ezzine,Mentee at Mila. Next: PhD with Yoshua Bengio at Mila )

Wisdom d'Almeida (Mentee at Mila-Quebec AI institute. Next: researcher at Miscrosoft/PhD at Oxford )

Jiahe Tian (Mentee at Harvard. Next: graduent student at CMU )

Ruobin Tao(Mentee in Boston, now at University of New South Wales, Australia )

Yuhao Qian (Mentee in Boston. Next: Amazon )

Pascal Junior Tikeng Notsawo (Mentee at Mila-Quebec AI institute. Next: PhD at Mila )

Leyu Dai (Mentee at Harvard. Next: PhD at University of North Carolina at Chapel Hill )

Junfeng Zhi (Mentee at Harvard. Next: graduent student at Duke. Now: engineer at Amazon )

Brice Nanda (Mentee at Mila-Quebec AI institute. Next:Msc at Mila )

Yihe Yang (Mentee at Harvard. Next:Msc at CMU )

Zhuang Ma (Mentee at Harvard. Next: graduent student at CMU )



  • [Nov 2023] Our exploration of generative models for causal discovery of gene networks Swift-DynGFN is accepted at Neurips Generative model for biology workshop. Congratulatiosn to Trang and all co-authors.
  • [Nov 2023] Make our large language model physical reasoning task COAT is available
  • [June. 2023] Our 2-year effort on attention schema will be presented at Neurips InforCog workshop
  • [Apr. 2023] Present our SAF paper at ICLR 2023 at Kigali, Rwanda
Join our team

Join our team

We have multiple openings for PhD students, postdocs, interns and visiting scholars to join our lab and work with us together in the following directions:

  • Discrete compositional representation
  • Causal Treatment/intervention effects prediction in medicine/biomedical sciences
We welcome any candidate who dare to think big,sees things differently and not fond of rules, regardless of their gender, religion, race, age, national origin,or disability. If you are interested, please fill in this form AND send me an email at

dianbo at nus dot edu dot sg

Selected Publications

Selected Publications


For the most up-to-date list of publications, see my Google Scholar profile.

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Trang Nguyen, Alexander Tong, Kanika Madan, Yoshua Bengio, Dianbo Liu
Attention Schema in Neural Agents
Dianbo Liu , Samuele Bolotta, He Zhu, Yoshua Bengio, Guillaume Dumas
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu , Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio
ICML 2023
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
Dianbo Liu, Vedant Shah,Oussama Boussif, Anirudh Goyal, Michael Curtis Mozer,Nicolas Heessm Yoshua Bengio.
ICLR 2023
Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data
Dianbo Liu , Karmel W Choi, Paulo Lizano, William Yuan, Kun-Hsing Yu, Jordan Smoller, Isaac Kohane
Machine learning approaches to predicting no-shows in pediatric medical appointment
Dianbo Liu, Won-Yong Shin, Eli Sprecher, Kathleen Conroy, Omar Santiago, Gal Wachtel, Mauricio Santillana
NPJ digital medicine 2022
Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Bonaventure F. P. Dossou, Dianbo Liu, Xu Ji, Moksh Jain, Almer M. van der Sloot, Roger Palou, Michael Tyers, Yoshua Bengio
Discrete- Valued Neural Communication
Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio
Neurips 2021
FeARH: Federated machine learning with anonymous random hybridization on electronic medical records
Jianfei Cui, He Zhu, Hao Deng, Ziwei Chen,Dianbo Liu
Journal of Biomedical Informatics 2021
ENCODE Phase III: Building an Encyclopedia of Candidate cis-Regulatory Elements for Human and Mouse
Jill Moore1, Michael J. Purcaro , Bradley E. Bernstein. . . Dianbo Liu. . . .. Barbara Wold, Ross C. Hardison , al.
Nature 2020
Patients with cancer appear more vulnerable to SARS-COV-2: a multicenter study during the COVID-19 outbreak
(co-first author) Dai, Mengyuan*, Dianbo Liu*, Miao Liu, Fuxiang Zhou, Guiling Li, Zhen Chen, Zhian Zhang et al.
Cancer discovery 2020
Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization
Dianbo Liu, Jose Davila-Velderrain, Zhizhuo Zhang, Manolis Kellis al.
Nucleic acids research 2019
Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
Li Huang, Andrew L Shea, Huining Qian, Aditya Masurkar, Hao Deng, Dianbo Liu
Journal of biomedical informatics 2019
Get in Touch


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12 Science Drive 2, Singapore 117549